检索结果-内蒙古大学图书馆

Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Muhammad Hassan Shoaib Hassan Aniqa Mujahid Muhammad Umair Muhammad Zubair Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan School of Computer Science & Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Lung cancer, a leading cause of mortality, necessitates prompt detection for improved patient outcomes. Recent strides in diagnostic technologies, including biomarkers, genetic testing, and advanced imaging like CT and MRI, have revolutionized lung cancer detection. computer-aided diagnosis (CAD) tools offer invaluable insights, rapidly analyzing imaging data for early identification. Integration of machine learning, deep learning, and CNNs has further transformed lung cancer diagnosis, empowering pulmonologists with highly accurate and efficient tools for interpreting medical imaging. Feature extraction plays the most vital role in the detection of lung cancer. In this research, a hybrid approach is proposed for better feature extraction, and then different classifiers are used to classify it for efficient detection of lung cancer. The dataset is taken from IQ-OTH/NCCD and is publicly available on Kaggle. Median and Gaussian filters are used for data preprocessing. To enlarge the dataset and avoid the chances of overfitting, data augmentation is used. Then, feature extraction is done on the preprocessed dataset using handcrafted methods HOG, GLCM, and pretrained automated models DenseNet201 and VGG-16. Features extracted from these algorithms are concatenated into a single array. Different combinations are tested with SVM, XGBoost, Artificial Neural Network (ANN) classifiers, and Logistic Regression. Results are compared in accuracy, precision, recall, and score. The results obtained from these combinations after classifying them with SVM, ANN, XGBoost, and Logistic regression give efficient results, and one of the combinations outperforms the previous results, providing an accuracy of up to 99.3% after being classified with SVM.

关键词： Support vector machines Logistic regression Accuracy Filters Biological system modeling Lung cancer Artificial neural networks Feature extraction Data augmentation Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka 4

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

引用

4th International Conference on Advancements in Computing, ICAC 2022

作者： Jayawardena, Aparna Ganegoda, Kasuni Imbulana, Sakuni Gunapala, Gavin Kodagoda, Nuwan Jayasinghe, Thilini Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries;leaf disease identification;cover crop identification;and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology. © 2022 IEEE.

关键词： Rubber

来源：评论

学校读者我要写书评

暂无评论

Solar Observation with the Fourier Transform *** Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

引用

Research in Astronomy and Astrophysics 2023年第12期23卷 128-137页

作者： Xianyong Bai Zhiwei Feng Zhiyong Zhang Yuanyong Deng Xiaoming Zhu Kaifan Ji Song Feng Xiao Yang Yongliang Song National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and TechnologyKunming 650500China

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible *** accuracy is also influenced by the spectral resolution of the Fourier transform *** the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared *** measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),*** also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μ*** quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing *** removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar *** Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also *** method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

关键词： Sun infrared-methods observational-instrumentation interferometers

来源：评论

学校读者我要写书评

暂无评论

IoT-Enabled Office Space Management 2

IoT-Enabled Office Space Management

引用

2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Shetty, Keerthi Mudholkar, Megha Mudholkar, Pankaj Muralidhar, L.B. Swamy, T.N. Lakshmikanth, R. Department of Mca Udupi India Marwadi University Department of Computer Engineering Gujarat Rajkot360003 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India School of Commerce Department of Bms Karnataka Bengaluru India Dr. Ambedkar Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Adithya Institute of Technology Department of Management Studies Coimbatore India

ISBN: (纸本)9798350364699

Increasing the efficiency of office spaces through the implementation of technology that is connected to the Internet of Things (IoT). Through the use of Internet of Things bias, the research provides a comprehensive framework for optimising space application, perfecting energy effectiveness, and enhancing overall plant production throughout the facility. To cover and control many aspects of the workplace environment, including lighting, heating, cooling, and occupancy, the frame combines intelligent detectors, real-time data analytics, and automated systems. The approach allows for dynamic changes to office conditions that are based on current operation patterns. This brings about a reduction in functional costs and a reduction in energy waste. The approach is based on the analysis of data obtained from Internet of Things bias. In addition, the investigation investigates the perpetration issues and implicit benefits of the results of the Internet of Things, focussing on the impact that these results have on both functional effectiveness and hand pleasure. According to the findings, Internet of Things (IoT) -Enabled operation systems have the potential to drastically transform conventional office environments into workspaces that are more adaptable, efficient, and responsive. This would pave the way for future developments in smart structure technologies. © 2024 IEEE.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Non-Consensus Opinion Model with Byzantine Nodes 12

Non-Consensus Opinion Model with Byzantine Nodes

引用

12th International Workshop on Resilient Networks Design and Modeling, RNDM 2022

作者： Liu, Xinhan Achterberg, Massimo A. Kooij, Robert E. Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Unit Ict Tno Netherlands

ISBN: (数字)9781665486774

ISBN: (纸本)9781665486774

pinion dynamics models study how the interaction among people influences the opinion formation process. In most opinion dynamics models, only one opinion can exist in the steady state, which is different from the real-life opinion formation process. In 2009, Shao et at. introduced a Non-Consensus Opinion (NCO) model, which allows different opinions to coexist in the steady state. This paper extends the NCO model by introducing a special type of nodes, namely Byzantine nodes, to play the role of dishonest people. We perform simulations on three different network models: small-scale graphs, Erdős-Rényi random graphs and scale-free networks. We find a new steady state for the NCO model: the cyclic steady state. The cyclic behavior of the NCO and Byzantine NCO model is discussed, including a method to generate networks with extremely long cycle lengths. Other properties of the Byzantine NCO model, such as the probability of cyclic behavior and the final opinion distribution, are also studied. We find that the introduction of Byzantine nodes generally steers towards a more balanced steady state and increases the probability of cyclic behavior. The latter is particularly problematic in communication systems, where the large cycle lengths may cause a very slow consensus process and thus stalling future communications. © 2022 IEEE.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

Five-Stage Pipelined 32-Bit RISC-V Base Integer Instruction Set Architecture Soft Microprocessor Core in VHDL 23

Five-Stage Pipelined 32-Bit RISC-V Base Integer Instruction ...

引用

23rd International Seminar on Intelligent technology and Its Applications, ISITIA 2022

作者： Phangestu, Aaron Elson Mujiono, Ir. Totok Kom, M.I. Ahmad Zaini, St Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Computer Engineering Department Surabaya60111 Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

Proprietary technologies with complicated licensing currently dominate the microprocessor industry. As a result, we must seek out a freely available, open-source alternative. In this paper, we discussed the implementation of a five-stage pipelined soft processor core. The core uses the RISC-V RV32I Base Integer Instruction Set Architecture. RISC-V is an open standard ISA that is freely available to use and modify. To implement our processor core, we followed the FPGA design methodology. First, we implemented the design specification with the VHDL Hardware Description Language. Then, siwe mulated the design in the ModelSim simulation environment. Following the verification, we analyzed the resource usage, critical path, and maximum frequency of the processor, then uploaded the processor core to an actual Cyclone IV EP4CE6E22C FPGA. Our CPU core successfully executed all the RV32I instructions, except FENCE, ECALL, and CSR instructions. The proposed processor core runs on 2115 LUTs, 558 flip-flops, and 67,608 memory bits, with a maximum frequency of 62.95 MHz. © 2022 IEEE.

关键词： computer hardware description languages

来源：评论

学校读者我要写书评

暂无评论

Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

引用

SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Optimal scheduling of battery energy storage in microgrid to minimize electricity and fuel costs 7

Optimal scheduling of battery energy storage in microgrid to...

引用

7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Simic, Zvonimir Topic, Danijel Dubravac, Marina Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9789532901160

Optimal scheduling of the battery energy storage (BES) in a microgrid is presented in this paper. The microgrid model in this paper consists of a photovoltaic (PV) power plant, a wind generator (WG), battery energy storage (BES), load, and diesel generator (DG). BES is required to store the generation excess from renewable energy sources (RES). BES also can store energy from the main grid but optimization is required to decide when to charge and discharge the battery. The optimization goal is to minimize electricity costs when the main grid is available and fuel costs when it is not available. The optimization problem in this paper is a linear programming (LP) problem that is modeled and solved with Python Gurobi. Optimization results are presented for different cases. A DG is used only when the main grid is not available due to the high fuel price. Electricity cost from DG is 5.28 times higher than the cost from the grid for the first case and 4.36 times higher for the second case. There is no generation excess in the second case so the battery is not used when the main grid is not available. © 2022 University of Split, FESB.

关键词： Scheduling

来源：评论

学校读者我要写书评

暂无评论

Implementation of MTCNN Facial Feature Extraction on Sleepiness Scale Classification Using CNN

Implementation of MTCNN Facial Feature Extraction on Sleepin...

引用

2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Putra, Adima Mahardika Zaini, Ahmad Pramunanto, Eko Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Sleepiness is a condition when the level of human consciousness decreases. Sleepiness is not easy to measure externally. If this is allowed just like that, it would be very dangerous if we were doing activities that requires full control of consciousness such as activities driving. This set of tools and methods for detecting drowsiness has been developed. However, in its implementation the intrusive method uses this tool less practical. In addition, the sleep detection method uses video images as well experiencing problems due to the potential for data loss in the form of facial features. To get this data, you must use a near infrared camera which has a low resolution and lacks detail. Therefore it is necessary an algorithm that is able to detect facial features to the maximum for classifying a person's sleepiness scale. To achieve the goal, a program will be created that functions to perform the extraction facial features and eye features. The program will function to detect number of frames containing facial features and performing condition classification eyes closed or open which will then be saved in a 'csv' file to be processed. Furthermore, the data will be carried out in the training process using 1D CNN architecture. The results of the training process that has been carried out The previous model is the model that will be used in performing the scale classification drowsiness. There are 6 experimental scenarios to get the best results. From all the results that have been obtained, it can be concluded that the best model results are the result of the training process with epoch values by 30 and the addition of synthetic data. Accuracy value obtained of 89% and the loss value of 34%. © 2022 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Review Paper on Face Recognition Techniques 9

A Review Paper on Face Recognition Techniques

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Rashid Al-Bakri, Ibrahim Mahmood Imran Ahmad, Muhammad Md Isa, Mohd Nazrin Zuhaer Nayef Al-Dabagh, Mustafa Universiti Malaysia Perlis Faculty of Electronic Engineering Technology Perlis Malaysia Telafer University Collage of Agriculture Telafer Iraq Knowledge University Department of Computer Science Erbil Iraq

ISBN: (纸本)9798350397376

The study of computer vision and pattern recognition is growing because of the various commercial and practical applications of these disciplines. Identification of individuals in a multitude, access control, forensics, and human-computer interactions are only among the topics studied by these areas. However, analyzing unconstrained face recognition poses ethical issues and privacy concerns. Many recent proposals employ Holistic Methods, Geometric Approach and Local-Texture Approach, methods and databases like ORL, FERET and AR Dataset to study constrained face recognition. At least some understanding of 2D perspective was achieved. This occurred in highly controlled environments where parameters such as camera angles, lighting and distance were strictly regulated. However, significant degradation in recognition performance occurred if the environment changed or the subject smiled or frowned. This critique discusses the technology utilized in face recognition, as well as the databases of methods that utilize this technology. To help guide future research, this article discusses current knowledge and suggests future directions for study in the field of facial recognition. © 2023 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：